Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multi-model validation study
Human activity has a profound influence on river discharges, hydrological extremes and water-related hazards. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simulati...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2018-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/aab96f |
| _version_ | 1797748306228543488 |
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| author | T I E Veldkamp F Zhao P J Ward H de Moel J C J H Aerts H Müller Schmied F T Portmann Y Masaki Y Pokhrel X Liu Y Satoh D Gerten S N Gosling J Zaherpour Y Wada |
| author_facet | T I E Veldkamp F Zhao P J Ward H de Moel J C J H Aerts H Müller Schmied F T Portmann Y Masaki Y Pokhrel X Liu Y Satoh D Gerten S N Gosling J Zaherpour Y Wada |
| author_sort | T I E Veldkamp |
| collection | DOAJ |
| description | Human activity has a profound influence on river discharges, hydrological extremes and water-related hazards. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simulation of mean, high- and low-flows. The analysis is performed for 471 gauging stations across the globe for the period 1971–2010. We find that the inclusion of HIP improves the performance of the GHMs, both in managed and near-natural catchments. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments. This finding is robust across the GHMs, although the level of improvement and the reasons for it vary greatly. The inclusion of HIP leads to a significant decrease in the bias of the long-term mean monthly discharge in 36%–73% of the studied catchments, and an improvement in the modeled hydrological variability in 31%–74% of the studied catchments. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. Whilst the inclusion of HIP leads to decreases in the simulated high-flows, it can lead to either increases or decreases in the low-flows. This is due to the relative importance of the timing of return flows and reservoir operations as well as their associated uncertainties. Even with the inclusion of HIP, we find that the model performance is still not optimal. This highlights the need for further research linking human management and hydrological domains, especially in those areas in which human impacts are dominant. The large variation in performance between GHMs, regions and performance indicators, calls for a careful selection of GHMs, model components and evaluation metrics in future model applications. |
| first_indexed | 2024-03-12T16:03:00Z |
| format | Article |
| id | doaj.art-d1cd502a29f34f65adc9a35684c6303d |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T16:03:00Z |
| publishDate | 2018-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-d1cd502a29f34f65adc9a35684c6303d2023-08-09T14:32:54ZengIOP PublishingEnvironmental Research Letters1748-93262018-01-0113505500810.1088/1748-9326/aab96fHuman impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multi-model validation studyT I E Veldkamp0https://orcid.org/0000-0002-2295-8135F Zhao1https://orcid.org/0000-0002-4819-3724P J Ward2https://orcid.org/0000-0001-7702-7859H de Moel3J C J H Aerts4H Müller Schmied5https://orcid.org/0000-0001-5330-9923F T Portmann6Y Masaki7Y Pokhrel8X Liu9https://orcid.org/0000-0001-5726-7353Y Satoh10D Gerten11S N Gosling12https://orcid.org/0000-0001-5973-6862J Zaherpour13Y Wada14Institute for Environmental Studies (IVM) , VU Amsterdam, the Netherlands; International Institute for Applied Systems Analysis , Laxenburg, Austria; Author to whom any correspondence should be addressed.Potsdam Institute for Climate Impact Research , Potsdam, GermanyInstitute for Environmental Studies (IVM) , VU Amsterdam, the NetherlandsInstitute for Environmental Studies (IVM) , VU Amsterdam, the NetherlandsInstitute for Environmental Studies (IVM) , VU Amsterdam, the Netherlands; Department of Geography , University of California, Santa Barbara, Santa Barbara, United States of AmericaInstitute of Physical Geography , Goethe-University Frankfurt, Frankfurt, Germany; Senckenberg Biodiversity and Climate Research Centre (SBiK-F) , Frankfurt, GermanyInstitute of Physical Geography , Goethe-University Frankfurt, Frankfurt, GermanyNational Institute for Environmental Studies , Tsukuba, JapanDepartment of Civil and Environmental Engineering , Michigan State University, Michigan, United States of AmericaKey Laboratory of Water Cycle and Related Land Surface Processes , Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaInternational Institute for Applied Systems Analysis , Laxenburg, AustriaPotsdam Institute for Climate Impact Research , Potsdam, Germany; Department of Geography , Humboldt-Universität zu Berlin, Berlin, GermanySchool of Geography , University of Nottingham, Nottingham, United KingdomSchool of Geography , University of Nottingham, Nottingham, United KingdomInternational Institute for Applied Systems Analysis , Laxenburg, Austria; Department of Physical Geography , Utrecht University, the NetherlandsHuman activity has a profound influence on river discharges, hydrological extremes and water-related hazards. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simulation of mean, high- and low-flows. The analysis is performed for 471 gauging stations across the globe for the period 1971–2010. We find that the inclusion of HIP improves the performance of the GHMs, both in managed and near-natural catchments. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments. This finding is robust across the GHMs, although the level of improvement and the reasons for it vary greatly. The inclusion of HIP leads to a significant decrease in the bias of the long-term mean monthly discharge in 36%–73% of the studied catchments, and an improvement in the modeled hydrological variability in 31%–74% of the studied catchments. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. Whilst the inclusion of HIP leads to decreases in the simulated high-flows, it can lead to either increases or decreases in the low-flows. This is due to the relative importance of the timing of return flows and reservoir operations as well as their associated uncertainties. Even with the inclusion of HIP, we find that the model performance is still not optimal. This highlights the need for further research linking human management and hydrological domains, especially in those areas in which human impacts are dominant. The large variation in performance between GHMs, regions and performance indicators, calls for a careful selection of GHMs, model components and evaluation metrics in future model applications.https://doi.org/10.1088/1748-9326/aab96fhydrological extremeshuman impactvalidationglobal hydrological modelingmulti-modelfresh water resources |
| spellingShingle | T I E Veldkamp F Zhao P J Ward H de Moel J C J H Aerts H Müller Schmied F T Portmann Y Masaki Y Pokhrel X Liu Y Satoh D Gerten S N Gosling J Zaherpour Y Wada Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multi-model validation study Environmental Research Letters hydrological extremes human impact validation global hydrological modeling multi-model fresh water resources |
| title | Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multi-model validation study |
| title_full | Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multi-model validation study |
| title_fullStr | Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multi-model validation study |
| title_full_unstemmed | Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multi-model validation study |
| title_short | Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multi-model validation study |
| title_sort | human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes a multi model validation study |
| topic | hydrological extremes human impact validation global hydrological modeling multi-model fresh water resources |
| url | https://doi.org/10.1088/1748-9326/aab96f |
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